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Band 21

Recent Advances in Example-Based Machine Translation

Fr. 192.00

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.06.2003

Herausgeber

M. Carl + weitere

Verlag

Springer Netherland

Seitenzahl

482

Maße (L/B/H)

24/16/2.8 cm

Gewicht

721 g

Auflage

Softcover reprint of the original 1st edition 2003

Sprache

Englisch

ISBN

978-1-4020-1401-7

Beschreibung

Rezension

Michael Carl and Andy Way have done the field of MT and that of natural language processing in general a big favor by producing this collection. This is the first (fat) book-sized collection of current research in this fascinating area of MT, and it contains a number of chapters that will bring anyone interested quickly up to speed in this research area. ...


I would recommend this book to everyone active or interested in MT, and especially the papers of the foundational part I to computational linguistics researchers in general.


Reviewed by:

Walter Daelemans,

University of Antwerp


in:
Computational Linguistics
, Volume 30, Number 4

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.06.2003

Herausgeber

Verlag

Springer Netherland

Seitenzahl

482

Maße (L/B/H)

24/16/2.8 cm

Gewicht

721 g

Auflage

Softcover reprint of the original 1st edition 2003

Sprache

Englisch

ISBN

978-1-4020-1401-7

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Recent Advances in Example-Based Machine Translation
  • Produktbild: Recent Advances in Example-Based Machine Translation
  • I Foundations of EBMT.- 1 An Overview of EBMT.- 2 What is Example-Based Machine Translation?.- 3 Example-Based Machine Translation in a Controlled Environment.- 4 EBMT Seen as Case-based Reasoning.- II Run-time Approaches to EBMT.- 5 Formalizing Translation Memory.- 6 EBMT Using DP-Matching Between Word Sequences.- 7 A Hybrid Rule and Example-Based Method for Machine Translation.- 8 EBMT of POS-Tagged Sentences via Inductive Learning.- III Template-Driven EBMT.- 9 Learning Translation Templates from Bilingual Translation Examples.- 10 Clustered Transfer Rule Induction for Example-Based Translation.- 11 Translation Patterns, Linguistic Knowledge and Complexity in EBMT.- 12 Inducing Translation Grammars from Bracketed Alignments.- IV EBMT and Derivation Trees.- 13 Extracting Translation Knowledge from Parallel Corpora.- 14 Finding Translation Patterns from Dependency Structures.- 15 A Best-First Alignment Algorithm for Extraction of Transfer Mappings.- 16 Translating with Examples: The LFG-DOT Models of Translation.